Age-related macular degeneration (AMD) is a common debilitating disease whose public health impact has been increasing due to population aging. Our group has received NEI funding to ascertain a large data set of AMD patients, their affected and unaffected relatives and a group of unrelated control individuals with confirmed absence of AMD for studying the genetic epidemiology of this disorder. Our data set includes extensive information about environmental risk factors for AMD. Our hypothesis is that the incorporation of environmental covariates and detailed phenotypic features can enhance the gene mapping process. Methodological work not funded by the parent grant is needed to investigate statistical approaches for accomplishing this goal. Specific Aims: (1) Evaluate different approaches for incorporating phenotypic features of AMD into genetic linkage analysis. (2) Evaluate whether sophisticated epidemiologic risk-prediction models for environmental covariates, such as smoking history, can benefit our gene identification efforts. (3) Extend a conditional logistic regression model for candidate gene analysis to jointly analyze related cases, sibling and population controls, and examine its ability to estimate gene-environment (GxE) interaction. Methods: We will perform several simulation studies to 1) investigate the statistical power for detection of linkage signals when disease severity measures are analyzed as a covariate in affected sibling-pair (ASP) linkage analysis and as a quantitative trait in a Haseman-Elston regression model, 2) compare the statistical power of different techniques for incorporating information gained from epidemiologic modeling into ASP linkage analysis, and 3) evaluate the statistical power of the conditional logistic regression model and its extension to detect GxE interaction. We will apply these methods to our ongoing follow-up study of linkage signals obtained in an initial genome screen, to further analysis of our most promising AMD candidate gene, the apolipoprotein E (APOE) gene, and to future candidate gene analyses. Significance: Our long-term goal is to advance our knowledge about the genetic and environmental risk factors for AMD to ultimately aid in the development of better prevention and treatment strategies.